immunohistochemistry skimage.data.immunohistochemistry() [source] Immunohistochemical (IHC) staining with hematoxylin counterstaining. This picture shows colonic glands where the IHC expression of FHL2 protein is revealed with DAB. Hematoxylin counterstaining is applied to enhance the negative parts of the tissue. This image was acquired at the Center for Microscopy And Molecular Imaging (CMMI). No known copyright restrictions.

text skimage.data.text() [source] Gray-level “text” image used for corner detection. Notes This image was downloaded from Wikipedia <http://en.wikipedia.org/wiki/File:Corner.png>`__. No known copyright restrictions, released into the public domain.

lab2xyz skimage.color.lab2xyz(lab, illuminant='D65', observer='2') [source] CIE-LAB to XYZcolor space conversion. Parameters: lab : array_like The image in lab format, in a 3-D array of shape (.., .., 3). illuminant : {“A”, “D50”, “D55”, “D65”, “D75”, “E”}, optional The name of the illuminant (the function is NOT case sensitive). observer : {“2”, “10”}, optional The aperture angle of the observer. Returns: out : ndarray The image in XYZ format, in a 3-D array of shape (.., .., 3).

perimeter skimage.measure.perimeter(image, neighbourhood=4) [source] Calculate total perimeter of all objects in binary image. Parameters: image : array Binary image. neighbourhood : 4 or 8, optional Neighborhood connectivity for border pixel determination. Returns: perimeter : float Total perimeter of all objects in binary image. References [R282] K. Benkrid, D. Crookes. Design and FPGA Implementation of a Perimeter Estimator. The Queen’s University of Belfast. http://www.cs.qub.a

medial_axis skimage.morphology.medial_axis(image, mask=None, return_distance=False) [source] Compute the medial axis transform of a binary image Parameters: image : binary ndarray, shape (M, N) The image of the shape to be skeletonized. mask : binary ndarray, shape (M, N), optional If a mask is given, only those elements in image with a true value in mask are used for computing the medial axis. return_distance : bool, optional If true, the distance transform is returned as well as the

rgbcie2rgb skimage.color.rgbcie2rgb(rgbcie) [source] RGB CIE to RGB color space conversion. Parameters: rgbcie : array_like The image in RGB CIE format, in a 3-D array of shape (.., .., 3). Returns: out : ndarray The image in RGB format, in a 3-D array of shape (.., .., 3). Raises: ValueError If rgbcie is not a 3-D array of shape (.., .., 3). References [R54] http://en.wikipedia.org/wiki/CIE_1931_color_space Examples >>> from skimage import data >>> from skimage

dtype_limits skimage.dtype_limits(image, clip_negative=True) [source] Return intensity limits, i.e. (min, max) tuple, of the image’s dtype. Parameters: image : ndarray Input image. clip_negative : bool If True, clip the negative range (i.e. return 0 for min intensity) even if the image dtype allows negative values.

multiblock_lbp skimage.feature.multiblock_lbp(int_image, r, c, width, height) [source] Multi-block local binary pattern (MB-LBP). The features are calculated similarly to local binary patterns (LBPs), (See local_binary_pattern()) except that summed blocks are used instead of individual pixel values. MB-LBP is an extension of LBP that can be computed on multiple scales in constant time using the integral image. Nine equally-sized rectangles are used to compute a feature. For each rectangle, t

hprewitt skimage.filters.hprewitt(*args, **kwargs) [source] Deprecated function. Use skimage.filters.prewitt_h instead. Find the horizontal edges of an image using the Prewitt transform. Parameters: image : 2-D array Image to process. mask : 2-D array, optional An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. Returns: output : 2-D array The absolute Prewitt edg

corner_harris skimage.feature.corner_harris(image, method='k', k=0.05, eps=1e-06, sigma=1) [source] Compute Harris corner measure response image. This corner detector uses information from the auto-correlation matrix A: A = [(imx**2) (imx*imy)] = [Axx Axy] [(imx*imy) (imy**2)] [Axy Ayy] Where imx and imy are first derivatives, averaged with a gaussian filter. The corner measure is then defined as: det(A) - k * trace(A)**2 or: 2 * det(A) / (trace(A) + eps) Parameters: image : nd